PEPERCORN: Inferring Performance Models from Location Tracking Data
نویسندگان
چکیده
Stochastic performance models are widely used to analyse the performance of systems that process customers and resources. However, the construction of such models is traditionally manual and therefore expensive, intrusive and prone to human error. In this paper we introduce PEPERCORN, a Petri Net Performance Model (PNPM) construction tool, which, given a dataset of raw location tracking traces obtained from a customer-processing system, automatically formulates and parameterises a corresponding Coloured Generalised Stochastic Petri Net (CGSPN) performance model.
منابع مشابه
Automated construction of Petri net performance models from high-precision location tracking data
Stochastic performance models are widely used to analyse the performance and reliability of systems that involve the flow and processing of customers and resources. However, model formulation and parameterisation are traditionally manual and thus expensive, intrusive and error-prone. This thesis illustrates the feasibility of automated performance model construction from highprecision location ...
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